SoroushVahidi/scidocs huggingface dataset:
A CNS-permeable Hsp90 inhibitor rescues synaptic dysfunction and memory loss in APP-overexpressing Alzheimer’s mouse model via an HSF1-mediated mechanism
(2016)

Induction of neuroprotective heat-shock proteins via pharmacological Hsp90 inhibitors is currently being investigated as a potential treatment for neurodegenerative diseases. Two major hurdles for therapeutic use of Hsp90 inhibitors are systemic toxicity and limited central nervous system permeability. We demonstrate here that chronic treatment with a proprietary Hsp90 inhibitor compound (OS47720) not only elicits a heat-shock-like response but also offers synaptic protection in symptomatic Tg2576 mice, a model of Alzheimer’s disease, without noticeable systemic toxicity. Despite a short half-life of OS47720 in mouse brain, a single intraperitoneal injection induces rapid and long-lasting (>3 days) nuclear activation of the heat-shock factor, HSF1. Mechanistic study indicates that the remedial effects of OS47720 depend upon HSF1 activation and the subsequent HSF1-mediated transcriptional events on synaptic genes. Taken together, this work reveals a novel role of HSF1 in synaptic function and memory, which likely occurs through modulation of the synaptic transcriptome.
Credit card fraud detection using machine learning techniques: A comparative analysis
(2017)

Financial fraud is an ever growing menace with far consequences in the financial industry. Data mining had played an imperative role in the detection of credit card fraud in online transactions. Credit card fraud detection, which is a data mining problem, becomes challenging due to two major reasons — first, the profiles of normal and fraudulent behaviours change constantly and secondly, credit card fraud data sets are highly skewed. The performance of fraud detection in credit card transactions is greatly affected by the sampling approach on dataset, selection of variables and detection technique(s) used. This paper investigates the performance of naïve bayes, k-nearest neighbor and logistic regression on highly skewed credit card fraud data. Dataset of credit card transactions is sourced from European cardholders containing 284,807 transactions. A hybrid technique of under-sampling and oversampling is carried out on the skewed data. The three techniques are applied on the raw and preprocessed data. The work is implemented in Python. The performance of the techniques is evaluated based on accuracy, sensitivity, specificity, precision, Matthews correlation coefficient and balanced classification rate. The results shows of optimal accuracy for naïve bayes, k-nearest neighbor and logistic regression classifiers are 97.92%, 97.69% and 54.86% respectively. The comparative results show that k-nearest neighbour performs better than naïve bayes and logistic regression techniques.
Bangladeshi Number Plate Detection: Cascade Learning vs. Deep Learning
(2017)

This work investigated two different machine learning techniques: Cascade Learning and Deep Learning, to find out which algorithm performs better to detect the number plate of vehicles registered in Bangladesh. To do this, we created a dataset of about 1000 images collected from a security camera of Independent University, Bangladesh. Each image in the dataset were then labelled manually by selecting the Region of Interest (ROI). In the Cascade Learning approach, a sliding window technique was used to detect objects. Then a cascade classifier was employed to determine if the window contained object of interest or not. In the Deep Learning approach, CIFAR-10 dataset was used to pre-train a 15-layer Convolutional Neural Network (CNN). Using this pretrained CNN, a Regions with CNN (R-CNN) was then trained using our dataset. We found that the Deep Learning approach (maximum accuracy 99.60% using 566 training images) outperforms the detector constructed using Cascade classifiers (maximum accuracy 59.52% using 566 positive and 1022 negative training images) for 252 test images.
Combining Poisson singular integral and total variation prior models in image restoration
(2014)

In this paper, a novel Bayesian image restoration method based on a combination of priors is presented. It is well known that the Total Variation (TV) image prior preserves edge structures while imposing smoothness on the solutions. However, it tends to oversmooth textured areas. To alleviate this problem we propose to combine the TV and the Poisson Singular Integral (PSI) models, which, as we will show, preserves the image textures. The PSI prior depends on a parameter that controls the shape of the filter. A study on the behavior of the filter as a function of this parameter is presented. Our restoration model utilizes a bound for the TV image model based on the majorization–minimization principle, and performs maximum a posteriori Bayesian inference. In order to assess the performance of the proposed approach, in the experimental section we compare it with other restoration methods. & 2013 Elsevier B.V. All rights reserved.
A High-Quality Denoising Dataset for Smartphone Cameras
(2018)

The last decade has seen an astronomical shift from imaging with DSLR and point-and-shoot cameras to imaging with smartphone cameras. Due to the small aperture and sensor size, smartphone images have notably more noise than their DSLR counterparts. While denoising for smartphone images is an active research area, the research community currently lacks a denoising image dataset representative of real noisy images from smartphone cameras with high-quality ground truth. We address this issue in this paper with the following contributions. We propose a systematic procedure for estimating ground truth for noisy images that can be used to benchmark denoising performance for smartphone cameras. Using this procedure, we have captured a dataset - the Smartphone Image Denoising Dataset (SIDD) - of ~30,000 noisy images from 10 scenes under different lighting conditions using five representative smartphone cameras and generated their ground truth images. We used this dataset to benchmark a number of denoising algorithms. We show that CNN-based methods perform better when trained on our high-quality dataset than when trained using alternative strategies, such as low-ISO images used as a proxy for ground truth data.
THE CONTROL OF THE FALSE DISCOVERY RATE IN MULTIPLE TESTING UNDER DEPENDENCY By
(2001)

Benjamini and Hochberg suggest that the false discovery rate may be the appropriate error rate to control in many applied multiple testing problems. A simple procedure was given there as an FDR controlling procedure for independent test statistics and was shown to be much more powerful than comparable procedures which control the traditional familywise error rate. We prove that this same procedure also controls the false discovery rate when the test statistics have positive regression dependency on each of the test statistics corresponding to the true null hypotheses. This condition for positive dependency is general enough to cover many problems of practical interest, including the comparisons of many treatments with a single control, multivariate normal test statistics with positive correlation matrix and multivariate t. Furthermore, the test statistics may be discrete, and the tested hypotheses composite without posing special difficulties. For all other forms of dependency, a simple conservative modification of the procedure controls the false discovery rate. Thus the range of problems for which a procedure with proven FDR control can be offered is greatly increased.
Gesture Recognition with a Convolutional Long Short-Term Memory Recurrent Neural Network
(2016)

Inspired by the adequacy of convolutional neural networks in implicit extraction of visual features and the efficiency of Long Short-Term Memory Recurrent Neural Networks in dealing with long-range temporal dependencies, we propose a Convolutional Long Short-Term Memory Recurrent Neural Network (CNNLSTM) for the problem of dynamic gesture recognition. The model is able to successfully learn gestures varying in duration and complexity and proves to be a significant base for further development. Finally, the new gesture command TsironiGR-dataset for human-robot interaction is presented for the evaluation of CNNLSTM.
Mirroring others' emotions relates to empathy and interpersonal competence in children
(2008)

The mirror neuron system (MNS) has been proposed to play an important role in social cognition by providing a neural mechanism by which others' actions, intentions, and emotions can be understood. Here functional magnetic resonance imaging was used to directly examine the relationship between MNS activity and two distinct indicators of social functioning in typically-developing children (aged 10.1 years+/-7 months): empathy and interpersonal competence. Reliable activity in pars opercularis, the frontal component of the MNS, was elicited by observation and imitation of emotional expressions. Importantly, activity in this region (as well as in the anterior insula and amygdala) was significantly and positively correlated with established behavioral measures indexing children's empathic behavior (during both imitation and observation) and interpersonal skills (during imitation only). These findings suggest that simulation mechanisms and the MNS may indeed be relevant to social functioning in everyday life during typical human development.
Genetic and environmental influences on sexual orientation and its correlates in an Australian twin sample.
(2000)

We recruited twins systematically from the Australian Twin Registry and assessed their sexual orientation and 2 related traits: childhood gender nonconformity and continuous gender identity. Men and women differed in their distributions of sexual orientation, with women more likely to have slight-to-moderate degrees of homosexual attraction, and men more likely to have high degrees of homosexual attraction. Twin concordances for nonheterosexual orientation were lower than in prior studies. Univariate analyses showed that familial factors were important for all traits, but were less successful in distinguishing genetic from shared environmental influences. Only childhood gender nonconformity was significantly heritable for both men and women. Multivariate analyses suggested that the causal architecture differed between men and women, and, for women, provided significant evidence for the importance of genetic factors to the traits' covariation.
Retro: Targeted Resource Management in Multi-tenant Distributed Systems
(2015)

In distributed systems shared by multiple tenants, effective resource management is an important pre-requisite to providing quality of service guarantees. Many systems deployed today lack performance isolation and experience contention, slowdown, and even outages caused by aggressive workloads or by improperly throttled maintenance tasks such as data replication. In this work we present Retro, a resource management framework for shared distributed systems. Retro monitors per-tenant resource usage both within and across distributed systems, and exposes this information to centralized resource management policies through a high-level API. A policy can shape the resources consumed by a tenant using Retro’s control points, which enforce sharing and ratelimiting decisions. We demonstrate Retro through three policies providing bottleneck resource fairness, dominant resource fairness, and latency guarantees to high-priority tenants, and evaluate the system across five distributed systems: HBase, Yarn, MapReduce, HDFS, and Zookeeper. Our evaluation shows that Retro has low overhead, and achieves the policies’ goals, accurately detecting contended resources, throttling tenants responsible for slowdown and overload, and fairly distributing the remaining cluster capacity.
Significant Change Spotting for Periodic Human Motion Segmentation of Cleaning Tasks Using Wearable Sensors
(2017)

The proportion of the aging population is rapidly increasing around the world, which will cause stress on society and healthcare systems. In recent years, advances in technology have created new opportunities for automatic activities of daily living (ADL) monitoring to improve the quality of life and provide adequate medical service for the elderly. Such automatic ADL monitoring requires reliable ADL information on a fine-grained level, especially for the status of interaction between body gestures and the environment in the real-world. In this work, we propose a significant change spotting mechanism for periodic human motion segmentation during cleaning task performance. A novel approach is proposed based on the search for a significant change of gestures, which can manage critical technical issues in activity recognition, such as continuous data segmentation, individual variance, and category ambiguity. Three typical machine learning classification algorithms are utilized for the identification of the significant change candidate, including a Support Vector Machine (SVM), k-Nearest Neighbors (kNN), and Naive Bayesian (NB) algorithm. Overall, the proposed approach achieves 96.41% in the F1-score by using the SVM classifier. The results show that the proposed approach can fulfill the requirement of fine-grained human motion segmentation for automatic ADL monitoring.
Decontaminating eukaryotic genome assemblies with machine learning
(2017)

High-throughput sequencing has made it theoretically possible to obtain high-quality de novo assembled genome sequences but in practice DNA extracts are often contaminated with sequences from other organisms. Currently, there are few existing methods for rigorously decontaminating eukaryotic assemblies. Those that do exist filter sequences based on nucleotide similarity to contaminants and risk eliminating sequences from the target organism. We introduce a novel application of an established machine learning method, a decision tree, that can rigorously classify sequences. The major strength of the decision tree is that it can take any measured feature as input and does not require a priori identification of significant descriptors. We use the decision tree to classify de novo assembled sequences and compare the method to published protocols. A decision tree performs better than existing methods when classifying sequences in eukaryotic de novo assemblies. It is efficient, readily implemented, and accurately identifies target and contaminant sequences. Importantly, a decision tree can be used to classify sequences according to measured descriptors and has potentially many uses in distilling biological datasets.
SAX-EFG: an evolutionary feature generation framework for time series classification
(2014)

A variety of real world applications fit into the broad definition of time series classification. Using traditional machine learning approaches such as treating the time series sequences as high dimensional vectors have faced the well known "curse of dimensionality" problem. Recently, the field of time series classification has seen success by using preprocessing steps that discretize the time series using a Symbolic Aggregate ApproXimation technique (SAX) and using recurring subsequences ("motifs") as features.
 In this paper we explore a feature construction algorithm based on genetic programming that uses SAX-generated motifs as the building blocks for the construction of more complex features. The research shows that the constructed complex features improve the classification accuracy in a statistically significant manner for many applications.
Conserving Energy Through Neural Prediction of Sensed Data
(2015)

The constraint of energy consumption is a serious problem in wireless sensor networks (WSNs). In this regard, many solutions for this problem have been proposed in recent years. In one line of research, scholars suggest data driven approaches to help conserve energy by reducing the amount of required communication in the network. This paper is an attempt in this area and proposes that sensors be powered on intermittently. A neural network will then simulate sensors’ data during their idle periods. The success of this method relies heavily on a high correlation between the points making a time series of sensed data. To demonstrate the effectiveness of the idea, we conduct a number of experiments. In doing so, we train a NAR network against various datasets of sensed humidity and temperature in different environments. By testing on actual data, it is shown that the predictions by the device greatly obviate the need for sensed data during sensors’ idle periods and save over 65 percent of energy.
Appropriate blood pressure control in hypertensive and normotensive type 2 diabetes mellitus: a summary of the ABCD trial
(2007)

The hypertensive and normotensive Appropriate Blood Pressure Control in Diabetes (ABCD) studies were prospective, randomized, interventional clinical trials with 5 years of follow-up that examined the role of intensive versus standard blood pressure control in a total of 950 patients with type 2 diabetes mellitus. In the hypertensive ABCD study, a significant decrease in mortality was detected in the intensive blood pressure control group when compared with the standard blood pressure control group. There was also a marked reduction in the incidence of myocardial infarction when patients were randomly assigned to initial antihypertensive therapy with angiotensin-converting-enzyme inhibition rather than calcium channel blockade. The results of the normotensive ABCD study included associations between intensive blood pressure control and significant slowing of the progression of nephropathy (as assessed by urinary albumin excretion) and retinopathy, and fewer strokes. In both the hypertensive and normotensive studies, mean renal function (as assessed by 24 h creatinine clearance) remained stable during 5 years of either intensive or standard blood pressure intervention in patients with normoalbuminuria (<30 mg/24 h) or microalbuminuria (30–300 mg/24 h) at baseline. By contrast, the rate of creatinine clearance in patients with overt diabetic nephropathy (>300 mg/24 h; albuminuria) at baseline decreased by an average of 5 ml/min/year in spite of either intensive or standard blood pressure control. Analysis of the results of 5 years of follow-up revealed a highly significant correlation of all-cause and cardiovascular mortality with left ventricular mass and severity of albuminuria.
Gaussian process factorization machines for context-aware recommendations
(2014)

Context-aware recommendation (CAR) can lead to significant improvements in the relevance of the recommended items by modeling the nuanced ways in which context influences preferences. The dominant approach in context-aware recommendation has been the multidimensional latent factors approach in which users, items, and context variables are represented as latent features in low-dimensional space. An interaction between a user, item, and a context variable is typically modeled as some linear combination of their latent features. However, given the many possible types of interactions between user, items and contextual variables, it may seem unrealistic to restrict the interactions among them to linearity.
 To address this limitation, we develop a novel and powerful non-linear probabilistic algorithm for context-aware recommendation using Gaussian processes. The method which we call Gaussian Process Factorization Machines (GPFM) is applicable to both the explicit feedback setting (e.g. numerical ratings as in the Netflix dataset) and the implicit feedback setting (i.e. purchases, clicks). We derive stochastic gradient descent optimization to allow scalability of the model. We test GPFM on five different benchmark contextual datasets. Experimental results demonstrate that GPFM outperforms state-of-the-art context-aware recommendation methods.
Single-layered complex-valued neural network for real-valued classification problems
(2009)

This paper presents two models of complex-valued neurons (CVNs) for real-valued classification problems, incorporating two newly-proposed activation functions, and presents their abilities as well as differences between them on benchmark problems. In both models, each real-valued input is encoded into a phase between 0 and  of a complex number of unity magnitude, and multiplied by a complex-valued weight. The weighted sum of inputs is fed to an activation function. Activation functions of both models map complex values into real values, and their role is to divide the net-input (weighted sum) space into multiple regions representing the classes of input patterns. The gradient-based learning rule is derived for each of the activation functions. Ability of such CVNs are discussed and tested with two-class problems, such as two and three input Boolean problems, and symmetry detection in binary sequences. We exhibit here that both the models can form proper boundaries for these linear and nonlinear problems. For solving n-class problems, a complex-valued neural network (CVNN) consisting of n CVNs is also considered in this paper. We tested such single-layered CVNNs on several real world benchmark problems. The results show that the classification and generalization abilities of single-layered CVNNs are comparable to the conventional real-valued neural networks (RVNNs) having one hidden layer. Moreover, convergence of CVNNs is much faster than that of RVNNs in most of the cases.
Visual firewall: real-time network security monitor
(2005)

Networked systems still suffer from poor firewall configuration and monitoring. VisualFirewall seeks to aid in the configuration of firewalls and monitoring of networks by providing four simultaneous views that display varying levels of detail and time-scales as well as correctly visualizing firewall reactions to individual packets. The four implemented views, real-time traffic, visual signature, statistics, and IDS alarm, provide the levels of detail and temporality that system administrators need to properly monitor their systems in a passive or an active manner. We have visualized several attacks, and we feel that even individuals unfamiliar with networking concepts can quickly distinguish between benign and malignant traffic patterns with a minimal amount of introduction.
IDS rainStorm: visualizing IDS alarms
(2005)

The massive amount of alarm data generated from intrusion detection systems is cumbersome for network system administrators to analyze. Often, important details are overlooked and it is difficult to get an overall picture of what is occurring in the network by manually traversing textual alarm logs. We have designed a novel visualization to address this problem by showing alarm activity within a network. Alarm data is presented in an overview where system administrators can get a general sense of network activity and easily detect anomalies. They then have the option of zooming and drilling down for details. The information is presented with local network IP (Internet Protocol) addresses plotted over multiple yaxes to represent the location of alarms. Time on the x-axis is used to show the pattern of the alarms and variations in color encode the severity and amount of alarms. Based on our system administrator requirements study, this graphical layout addresses what system administrators need to see, is faster and easier than analyzing text logs, and uses visualization techniques to effectively scale and display the data. With this design, we have built a tool that effectively uses operational alarm log data generated on the Georgia Tech campus network. The motivation and background of our design is presented along with examples that illustrate its usefulness. CR Categories: C.2.0 [Computer-Communication Networks]: General—Security and Protection C.2.3 [ComputerCommunication Networks]: Network Operations—Network Monitoring H.5.2 [Information Systems]: Information Interfaces and Presentation—User Interfaces